Predictive maintenance, which leverages historical and real-time data to anticipate equipment failures, is pivotal in enhancing safety, reducing costs, extending machinery lifespan, and minimizing downtime across industries such as energy, transportation, and manufacturing. This approach uses sensors to track important parameters like pressure, vibration, and temperature. Machine learning and advanced data analysis are then used to analyze the data and predict failures. Through the optimization of maintenance schedules, the removal of unnecessary tasks, and the calculation of machinery’s remaining useful life, this proactive method enables maintenance staff to handle possible problems before they develop into expensive failures. Traditional classification methods typically rely on a single model’s predictions. However, ensemble classification amalgamates predictions from multiple models, harnessing various perspectives to enhance adaptability and accuracy. This study delves into the application of ensemble methods—including Gradient Boosting (GB), CatBoost Classifier, and Random Forest for the predictive maintenance of industrial machinery. Each ensemble technique is meticulously evaluated in diverse industrial contexts, with an emphasis on how varying hyperparameters influence model performance. The study proposes a method for optimizing results through parameter adjustment. By boosting efficiency and reliability, promoting innovation via IoT and AI integration, and ensuring infrastructure sustainability, predictive maintenance aligns with Sustainable Development Goal (SDG) 9. It also supports SDG 12 by maximizing resource utilization, reducing waste and environmental impact, promoting sustainable production, and cutting energy consumption and emissions. Additionally, by lowering carbon emissions, mitigating environmental risks, and extending machinery lifespan to reduce frequent replacements, it contributes to SDG 13. This paper provides valuable insights and guidance for researchers and industries seeking enhanced strategies for machinery predictive maintenance.